Tree stem detection is a key step toward retrieving detailed stem attributes from terrestrial laser scanning (TLS) data. Various point-based methods have been proposed for the stem point extraction at both individual tree and plot levels. The main limitation of the point-based methods is their high computing demand when dealing with plot-level TLS data. Although segment-based methods can reduce the computational burden and uncertainties of point cloud classification, its application is largely limited to urban scenes due to the complexity of the algorithm, as well as the conditions of natural forests. Here we propose a novel and simple segment-based method for efficient stem detection at the plot level, which is based on the curvature feature of the points and connected component segmentation. We tested our method using a public TLS dataset with six forest plots that were collected for the international TLS benchmarking project in Evo, Finland. Results showed that the mean accuracies of the stem point extraction were comparable to the state-of-art methods (>95%). The accuracies of the stem mappings were also comparable to the methods tested in the international TLS benchmarking project. Additionally, our method was applicable to a wide range of stem forms. In short, the proposed method is accurate and simple; it is a sensible solution for the stem detection of standing trees using TLS data.

Ground filtering is an essential step in airborne light detection and ranging (LiDAR) data processing in various applications. The cloth simulation filtering (CSF) algorithm has gained popularity because of its ease of use advantage. However, CSF has limitations in topographically and environmentally complex areas. Therefore, an improved CSF (ICSF) algorithm was developed in this study. ICSF uses morphological closing operations to initialize the cloth, and estimates the cloth rigidness for providing a more accurate reference terrain in various terrain characteristics. Moreover, terrain-adaptive height difference thresholds are developed for better filtering of airborne LiDAR point clouds. The performance of ICSF was assessed using International Society for Photogrammetry and Remote Sensing urban and rural samples and Open Topography forested samples. Results showed that ICSF can improve the filtering accuracy of CSF in the samples with various terrain and non-ground object characteristics, while maintaining the ease of use advantage of CSF. In urban and rural samples, ICSF obtained an average total error of 4.03% and outperformed another eight reference algorithms in terms of accuracy and robustness. In forested samples, ICSF produced more accuracy than the well-known filtering algorithms (including the maximum slope, progressive morphology, and cloth simulation filtering algorithms), and performed better with respect to the preservation of steep slopes and discontinuities and vegetation removal. Thus, the proposed algorithm can be used as an efficient tool for LiDAR data processing.

Canopy cover is an important indicator and commonly used in forest management applications. Unmanned-Aerial-Vehicle (UAV)—Borne Laser Scanning (ULS) has drawn increasing attention as a new alternative source for forest field inventory due to its spatial resolution comparable to that of Terrestrial Laser Scanning (TLS). In this study, the performance of plot canopy cover estimations from ULS and TLS is investigated. The experiment was conducted in 16 plots from two Pinus massoniana forests with different stand conditions in Guangxi, China. Both the Canopy Height Model (CHM)-based and Individual Tree Delineation (ITD)-based methods were used to estimate the canopy cover. The influence of CHM pixel sizes on the estimations was also analyzed. Our results demonstrated that the accuracies of ULS ($R^2$: 0.992–0.996, $RMSE$: 0.591–0.820%) were better than those of TLS ($R^2$: 0.541–0.846, $RMSE$: 3.642–6.297%) when compared against the reference. The average difference between the ULS and TLS estimations was 6.91%, and the disagreement increased as the forest complexity increased. The reasonable CHM pixel sizes for the canopy cover estimations were 0.07–1.2 m for ULS and 0.07–1.5 m for TLS. This study can provide useful information for the selection of data sources and estimation methods in plot canopy cover mapping.

Ground filtering is necessary in processing airborne light detection and ranging (LiDAR) point clouds for forestry applications. This study proposes a progressive plane detection filtering (PPDF) method. First, the method uses multi-scale planes to characterize terrain, i.e., the local terrain with large slope variations is represented by small-scale planes, and vice versa. The planes are detected in local point clouds by the random sample consensus method with decreasing plane sizes. The reliability of the planes to represent local terrain is evaluated and the planes with optimal sizes are selected according to evaluation results. Then, ground seeds are identified by selecting the interior points of the planes. Finally, ground points are iteratively extracted based on the reference terrain, which is constructed using evenly distributed neighbor ground points. These neighbor points are identified by selecting the nearest neighbor points of multiple subspaces, which are divided from the local space with an unclassified point as center point. PPDF was tested in six sites with various terrain and vegetation characteristics. Results showed that PPDF was more accurate and robust compared to the classic filtering methods including maximum slope, progressive morphology, cloth simulation, and progressive triangulated irregular network densification filtering methods, with the smallest average total error and standard deviation of 3.42% and 2.45% across all sites. Moreover, the sensitivity of PPDF to parameters was low and these parameters can be set as fixed values. Therefore, PPDF is effective and easy-to-use for filtering airborne LiDAR data.

Ground filtering is one of the essential steps for processing airborne light detection and ranging data in forestry applications. However, the performance of existing methods is still limited in forested areas due to the complex terrain and dense vegetation. To overcome this limitation, we proposed an improved surface-based filter based on multi-directional narrow window and cloth simulation. The innovations mainly involve two aspects as follows: (1) sufficient and uniformly distributed ground seeds are identified by merging the lowest points and line segments from the point clouds within a multi-directional narrow window; (2) complete and accurate ground points are extracted using a cyclic scheme that includes incorrect ground point elimination using the internal force adjustment of cloth simulation, terrain reconstruction with moving least-squares plane fitting, and ground point extraction based on progressively refined terrain. The proposed method was tested in five forested sites with various terrain characteristics and vegetation distributions. Experimental results showed that the proposed method could accurately separate ground points from non-ground points in different forested environments, with the average kappa coefficient of 88.51% and total error of 4.22%. Moreover, the comparative experiments proved that the proposed method performed better than the classical methods involving the slope-based, mathematical morphology-based and surface-based methods.